A compressive beamforming method

Gürbüz, Ali Cafer; McClellan, J.H.; Cevher, Volkan

doi:10.1109/icassp.2008.4518185

Cited by 118 publications

(69 citation statements)

References 7 publications

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“…When the source steering matrix satisfies the RIP property, it is known that the errors in the sparse vector estimates are well behaved under additive perturbations of the measurements [4]. In [8], we further discuss how each source can be modeled as additive noise in (4) and detail the construction of the steering matrices as a basis pursuit strategy.…”

Section: Estimation Of Steering Matricesmentioning

confidence: 99%

Compressive wireless arrays for bearing estimation

Cevher

Gürbüz

McClellan

et al. 2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, functions of the low dimensional random projections of the microphone signals can be used to determine multiple source bearings as a solution of an ℓ1-norm minimization problem. Field data results are shown where only 10bits of information is passed from each microphone to estimate multiple target bearings.

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Section: Estimation Of Steering Matricesmentioning

confidence: 99%

Compressive wireless arrays for bearing estimation

Cevher

Gürbüz

McClellan

et al. 2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

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“…In [42], a compressive wireless array is proposed for bearing estimation. In [43], a compressive beamforming method is presented. Both approaches apply compressive sampling in the time domain to reduce the ADC sampling rate or the number of time samples for each element of the array.…”

Section: Espirit Algorithmsmentioning

confidence: 99%

Beamforming for Direction-of-Arrival (DOA) Estimation-A Survey

Krishnaveni¹,

Kesavamurthy²,

Aparna.³

2013

IJCA

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Direction-of-Arrival (DOA) estimation plays a vital role in many applications. Beamforming is the most prominent technique to estimate DOA. In this survey, a study of various beamforming techniques and algorithms to estimate the direction of arrival of a signal is made. An assessment on the background robust algorithms using Nyquist sampling rate and its Compressive sensing alternative is done. It is known that Bearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, it may be concluded that, the methods those specifically exploits this spatial sparsity property is advantageous. These methods use a very small number of measurements in the form of random projections of the sensor data along with one full waveform recording at one of the sensors.

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“…Linear programming techniques, e.g., basis pursuit [1], or iterative greedy algorithms [9] can be used to solve (6). Distributed versions of CS have been considered in [2] and [5] in order to exploit the underlying correlation structures in the signal observations.…”

Section: Compressive Samplingmentioning

confidence: 99%

“…In this paper we propose a new architecture for array-based applications by exploiting the sparsity in the angle spectrum, with fun- damental differences from the approaches in [3] and [6]. Our CS array applies compressive sampling in the spatial domain, which compresses or transforms the array of a large number of elements into an array of a much smaller number of elements.…”

Section: Introductionmentioning

confidence: 99%

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Direction estimation using compressive sampling array processing

Wang

Leus

Pandharipande

2009

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

102

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We propose a new architecture, the compressive sampling (CS) array, for array based applications, by exploiting compressive sampling in the spatial domain. With random projections (or selections) of the array elements, we can transform a large size array into a small size array. We also propose two approaches of direction-of-arrival (DoA) estimation using our CS array, I. (joint) CS recovery, and II. CS beamformers. As a result we can greatly reduce the hardware complexity and software complexity while still maintaining the high resolution achieved as if a large size array were used.

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A compressive beamforming method

Cited by 118 publications

References 7 publications

Compressive wireless arrays for bearing estimation

Compressive wireless arrays for bearing estimation

Beamforming for Direction-of-Arrival (DOA) Estimation-A Survey

Direction estimation using compressive sampling array processing

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